import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class    snake_case       :




  def 						__lowercase(   self  :						List[Any]					)->							List[Any]:




       """simple docstring"""
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Any      =		TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'					)

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Any      =		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'					)

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Any      =		UNetaDConditionModel(
           sample_size=32  ,			layers_per_block=1  ,			block_out_channels=[32, 64]  ,			down_block_types=[
               'ResnetDownsampleBlock2D',
               'SimpleCrossAttnDownBlock2D',
           ]  ,			mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,			up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,			in_channels=3  ,			out_channels=6  ,			cross_attention_dim=32  ,			encoder_hid_dim=32  ,			attention_head_dim=8  ,			addition_embed_type='text'  ,			addition_embed_type_num_heads=2  ,			cross_attention_norm='group_norm'  ,			resnet_time_scale_shift='scale_shift'  ,			act_fn='gelu'  ,			)
       unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					int      =		DDPMScheduler(
           num_train_timesteps=1000  ,			beta_schedule='squaredcos_cap_v2'  ,			beta_start=0.0001  ,			beta_end=0.02  ,			thresholding=a_  ,			dynamic_thresholding_ratio=0.95  ,			sample_max_value=1.0  ,			prediction_type='epsilon'  ,			variance_type='learned_range'  ,			)

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		IFWatermarker()

       return {
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "watermarker": watermarker,
           "safety_checker": None,
           "feature_extractor": None,
       }




  def 						__lowercase(   self  :						Tuple					)->							int:




       """simple docstring"""
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'					)

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'					)

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		UNetaDConditionModel(
           sample_size=32  ,			layers_per_block=[1, 2]  ,			block_out_channels=[32, 64]  ,			down_block_types=[
               'ResnetDownsampleBlock2D',
               'SimpleCrossAttnDownBlock2D',
           ]  ,			mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,			up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,			in_channels=6  ,			out_channels=6  ,			cross_attention_dim=32  ,			encoder_hid_dim=32  ,			attention_head_dim=8  ,			addition_embed_type='text'  ,			addition_embed_type_num_heads=2  ,			cross_attention_norm='group_norm'  ,			resnet_time_scale_shift='scale_shift'  ,			act_fn='gelu'  ,			class_embed_type='timestep'  ,			mid_block_scale_factor=1.414  ,			time_embedding_act_fn='gelu'  ,			time_embedding_dim=32  ,			)
       unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					str      =		DDPMScheduler(
           num_train_timesteps=1000  ,			beta_schedule='squaredcos_cap_v2'  ,			beta_start=0.0001  ,			beta_end=0.02  ,			thresholding=a_  ,			dynamic_thresholding_ratio=0.95  ,			sample_max_value=1.0  ,			prediction_type='epsilon'  ,			variance_type='learned_range'  ,			)

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		DDPMScheduler(
           num_train_timesteps=1000  ,			beta_schedule='squaredcos_cap_v2'  ,			beta_start=0.0001  ,			beta_end=0.02  ,			)

       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		IFWatermarker()

       return {
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "image_noising_scheduler": image_noising_scheduler,
           "watermarker": watermarker,
           "safety_checker": None,
           "feature_extractor": None,
       }




  def 						__lowercase(   self  :						Optional[int]					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.pipeline_class(**a_					)
       pipe.to(a_					)
       pipe.set_progress_bar_config(disable=a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_dummy_inputs(a_					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		inputs['prompt']
       SCREAMING_SNAKE_CASE__					:					int      =		inputs['generator']
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		inputs['num_inference_steps']
       SCREAMING_SNAKE_CASE__					:					Dict      =		inputs['output_type']

       if "image" in inputs:
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		inputs['image']
       else:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		None

       if "mask_image" in inputs:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		inputs['mask_image']
       else:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		None

       if "original_image" in inputs:
            SCREAMING_SNAKE_CASE__					:					Tuple      =		inputs['original_image']
       else:
            SCREAMING_SNAKE_CASE__					:					Any      =		None

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Any      =		pipe.encode_prompt(a_					)

       # inputs with prompt converted to embeddings
       SCREAMING_SNAKE_CASE__					:					Any      =		{
           'prompt_embeds': prompt_embeds,
           'negative_prompt_embeds': negative_prompt_embeds,
           'generator': generator,
           'num_inference_steps': num_inference_steps,
           'output_type': output_type,
       }

       if image is not None:
            SCREAMING_SNAKE_CASE__					:					Dict      =		image

       if mask_image is not None:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		mask_image

       if original_image is not None:
            SCREAMING_SNAKE_CASE__					:					Dict      =		original_image

       # set all optional components to None
       for optional_component in pipe._optional_components:
            setattr(a_  ,			a_  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		pipe(**a_					)[0]

       with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.pipeline_class.from_pretrained(a_					)
            pipe_loaded.to(a_					)
            pipe_loaded.set_progress_bar_config(disable=a_					)

       pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

       for optional_component in pipe._optional_components:
            self.assertTrue(
                getattr(a_  ,			a_					) is None  ,			F'''`{optional_component}` did not stay set to None after loading.'''  ,			)

       SCREAMING_SNAKE_CASE__					:					int      =		self.get_dummy_inputs(a_					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		inputs['generator']
       SCREAMING_SNAKE_CASE__					:					str      =		inputs['num_inference_steps']
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		inputs['output_type']

       # inputs with prompt converted to embeddings
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		{
           'prompt_embeds': prompt_embeds,
           'negative_prompt_embeds': negative_prompt_embeds,
           'generator': generator,
           'num_inference_steps': num_inference_steps,
           'output_type': output_type,
       }

       if image is not None:
            SCREAMING_SNAKE_CASE__					:					List[str]      =		image

       if mask_image is not None:
            SCREAMING_SNAKE_CASE__					:					Tuple      =		mask_image

       if original_image is not None:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		original_image

       SCREAMING_SNAKE_CASE__					:					List[str]      =		pipe_loaded(**a_					)[0]

       SCREAMING_SNAKE_CASE__					:					Any      =		np.abs(to_np(a_					) - to_np(a_					)					).max()
       self.assertLess(a_  ,			1e-4					)




  def 						__lowercase(   self  :						Optional[Any]					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					:					Any      =		self.pipeline_class(**a_					)
       pipe.to(a_					)
       pipe.set_progress_bar_config(disable=a_					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.get_dummy_inputs(a_					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		pipe(**a_					)[0]

       with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.pipeline_class.from_pretrained(a_					)
            pipe_loaded.to(a_					)
            pipe_loaded.set_progress_bar_config(disable=a_					)

       pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

       SCREAMING_SNAKE_CASE__					:					int      =		self.get_dummy_inputs(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		pipe_loaded(**a_					)[0]

       SCREAMING_SNAKE_CASE__					:					Dict      =		np.abs(to_np(a_					) - to_np(a_					)					).max()
       self.assertLess(a_  ,			1e-4					)









'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     []
   for i in range(config.num_hidden_layers						):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")						)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ('cls_token', 'deit.embeddings.cls_token'),
           ('dist_token', 'deit.embeddings.distillation_token'),
           ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
           ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
           ('pos_embed', 'deit.embeddings.position_embeddings'),
       ]						)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ('norm.weight', 'layernorm.weight'),
              ('norm.bias', 'layernorm.bias'),
              ('pre_logits.fc.weight', 'pooler.dense.weight'),
              ('pre_logits.fc.bias', 'pooler.dense.bias'),
          ]						)

      # if just the base model, we should remove "deit" from all keys that start with "deit"
      A__       =     [(pair[0], pair[1][4:]) if pair[1].startswith('deit'						) else pair for pair in rename_keys]
   else:
      # layernorm + classification heads
      rename_keys.extend(
          [
              ('norm.weight', 'deit.layernorm.weight'),
              ('norm.bias', 'deit.layernorm.bias'),
              ('head.weight', 'cls_classifier.weight'),
              ('head.bias', 'cls_classifier.bias'),
              ('head_dist.weight', 'distillation_classifier.weight'),
              ('head_dist.bias', 'distillation_classifier.bias'),
          ]						)

   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   for i in range(config.num_hidden_layers						):
      if base_model:
         A__       =     ''
      else:
         A__       =     'deit.'
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""						)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""						)
      # next, add query, keys and values (in that order) to the state dict
      A__       =     in_proj_weight[
          : config.hidden_size, :
      ]
      A__       =     in_proj_bias[: config.hidden_size]
      A__       =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      A__       =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      A__       =     in_proj_weight[
          -config.hidden_size :, :
      ]
      A__       =     in_proj_bias[-config.hidden_size :]







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						)
   return im







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     DeiTConfig()
   # all deit models have fine-tuned heads
   A__       =     False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   A__       =     1000
   A__       =     'huggingface/label-files'
   A__       =     'imagenet-1k-id2label.json'
   A__       =     json.load(open(hf_hub_download(UpperCAmelCase      ,UpperCAmelCase      ,repo_type='dataset'						)      ,'r'						)						)
   A__       =     {int(UpperCAmelCase						): v for k, v in idalabel.items()}
   A__       =     idalabel
   A__       =     {v: k for k, v in idalabel.items()}
   A__       =     int(deit_name[-6:-4]						)
   A__       =     int(deit_name[-3:]						)
   # size of the architecture
   if deit_name[9:].startswith('tiny'						):
      A__       =     192
      A__       =     768
      A__       =     12
      A__       =     3
   elif deit_name[9:].startswith('small'						):
      A__       =     384
      A__       =     1536
      A__       =     12
      A__       =     6
   if deit_name[9:].startswith('base'						):
      pass
   elif deit_name[4:].startswith('large'						):
      A__       =     1024
      A__       =     4096
      A__       =     24
      A__       =     16

   # load original model from timm
   A__       =     timm.create_model(UpperCAmelCase      ,pretrained=UpperCAmelCase						)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   A__       =     timm_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase      ,UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
   read_in_q_k_v(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # load HuggingFace model
   A__       =     DeiTForImageClassificationWithTeacher(UpperCAmelCase						).eval()
   model.load_state_dict(UpperCAmelCase						)

   # Check outputs on an image, prepared by DeiTImageProcessor
   A__       =     int(
       (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
   A__       =     DeiTImageProcessor(size=UpperCAmelCase      ,crop_size=config.image_size						)
   A__       =     image_processor(images=prepare_img()      ,return_tensors='pt'						)
   A__       =     encoding['pixel_values']
   A__       =     model(UpperCAmelCase						)

   A__       =     timm_model(UpperCAmelCase						)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(UpperCAmelCase      ,outputs.logits      ,atol=1e-3						)

   Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
   print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""						)
   model.save_pretrained(UpperCAmelCase						)
   print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
   image_processor.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--deit_name''',
        default='''vit_deit_base_distilled_patch16_224''',
        type=str,
        help='''Name of the DeiT timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    lowerCAmelCase_     						=				parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


